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首页> 外文期刊>Annals. Computer Science Series >Application Of Dimensionality Reduction On Classification Of Colon Cancer Using Ica And K-Nn Algorithm
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Application Of Dimensionality Reduction On Classification Of Colon Cancer Using Ica And K-Nn Algorithm

机译：基于Ica和K-Nn算法的降维在结肠癌分类中的应用

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Several sectors including engineering, health, academics and so on deals with very large number of information and few specimens. This highlight the need of a technique to improve data accuracy in order to enable professionals such as biologists, clinicians and so on to comprehend the structure of a complex microarray dataset and the gene expression in cells when reduced. This study employs Independent Component Analysis for feature extraction before using k-Nearest Neighbor algorithm to classify colon cancer dataset which contains DNA microarray gene expression data with 2000 features and 62 samples. The experiment was performed using MATLAB 2015a. The result shows that the dimensionality reduction applied improve the classification performance in terms of accuracy, sensitivity, specificity and precision by 11.3%, 25.2%, 36.3% and 12.8% respectively.
机译：工程，卫生，学术界等多个部门处理的信息量非常大，标本很少。这突显了对提高数据准确性的技术的需求，以使诸如生物学家，临床医生等的专业人员能够理解复杂的微阵列数据集的结构以及减少时细胞中的基因表达。本研究采用独立成分分析进行特征提取，然后使用k-Nearest Neighbor算法对结肠癌数据集进行分类，该数据集包含具有2000个特征和62个样本的DNA微阵列基因表达数据。实验是使用MATLAB 2015a进行的。结果表明，降维方法在分类准确度，敏感性，特异性和准确性方面分别提高了11.3％，25.2％，36.3％和12.8％。

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